METHODS: An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined.
RESULTS: The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively.
CONCLUSIONS: This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.
方法:在深度学习模型中实现了枚举式预处理方法,以获得更好的DR严重程度分级精度。将所提出的方法与各种预训练模型进行比较,并列出了必要的性能指标。本文还对深度网络模型中使用的各种优化算法进行了比较分析,并概述了结果。
结果:在MESSIDOR数据集上进行实验结果以评估性能。实验结果表明,与其他组合相比,枚举管道组合K1-K2-K3-DFNN-LOA显示出更好的结果。与各种优化算法和预训练模型相比,所提出的模型具有更好的性能和最大的精度,精度,召回,F1得分,宏观平均指标为97.60%,94.60%,98.40%,94.60%,和0.97。
结论:本研究的重点是开发和实施彩色眼底照片DR筛查系统。这种基于人工智能的系统提供了增强DR诊断的有效性和可接近性的可能性。